Asked by Ahmed almansory
on 12 Apr 2013

after i complete my program,i want to save the output,weight and bias of hidden layer of neural network, can any one help me to do this? thanks

Answer by Greg Heath
on 15 Apr 2013

Accepted answer

close all, clear all, clc, plt=0;

delete h.mat delete LW.mat delete b2.mat delete tsettings.mat

[ x, t ] = simplefit_dataset; net = fitnet; rng(4151941) [ net tr y0 ] = train( net, x, t);

plt=plt+1,figure(plt) % figure 1 hold on plot(x,t,'b--','LineWidth',2) plot(x,y0,'r.','LineWidth',2) legend('TARGET','NN OUTPUT') xlabel('INPUT') ylabel('TARGETAND NN OUTPUT') title('SIMPLEFIT DATASET')

%Create tsettings, h, LW, b2

[xn xsettings] = mapminmax(x); [tn tsettings] = mapminmax(t); % SAVE tsettings b1 = cell2mat(net.b(1)) IW = cell2mat(net.IW) [ I N ] = size(xn) B1 = b1*ones(1,N); % B1 = repmat(b,1,N); % Alternate h = tanh(B1+IW*xn); % SAVE h LW = cell2mat(net.LW) % SAVE LW b2 = cell2mat(net.b(2)) % SAVE b2

clc whos h LW b2 tsettings dir % save FILENAME ... is the command form of the syntax % for convenient saving from the command line. With % command syntax, you do not need to enclose strings in % single quotation marks. Separate inputs with spaces % instead of commas. Do not use command syntax if % inputs such as FILENAME are variables.

save h save LW save b2 save tsettings dir

whos N h LW b2 tsettings disp('BEFORE CLEARING SAVED HIDDEN VARIABLES') disp('ENTER TO CONTINUE') pause dir

clear h LW b2 tsettings whos N h LW b2 tsettings disp('AFTER CLEARING SAVED HIDDEN VARIABLES') disp('ENTER TO CONTINUE') pause

load h.mat load LW b2 tsettings whos N h LW b2 tsettings disp('AFTER RELOADING SAVED HIDDEN VARIABLES') disp('ENTER TO CONTINUE') pause

yn = b2 + LW*h; y = mapminmax('reverse',yn,tsettings); reloadingerror = max(abs(y-y0))

break

% Hope this helps

**Thank you for formally accepting my answer**

Greg

Ahmed almansory
on 17 Apr 2013

thanks

Answer by Greg Heath
on 13 Apr 2013

Edited by Greg Heath
on 13 Apr 2013

clear all, clc [ x, t ] = simplefit_dataset; net0 = fitnet( 10 ); [ net0 tr0 y0 ] = train( net0, x, t); whos

%You can save the output and net

save y0 net0

%Delete them

clear y0 net0 whos

%and retrieve them

load y0 net0 whos

%or save, delete and and retrieve the weights

W0 = getwb(net0) save y0 W0 whos clear y0 W0 whos load y0 W0 whos

Hope this helps.

**Thank you for formally accepting my answer**

Greg

Ahmed almansory
on 13 Apr 2013

thanks for your replay my problem is : i design feed forward network for image compression consist of three layer (input,hidden,output)the number of neurons in both input and output layers is equal ,hidden layer contain less number of neuron for compression issue ,i get good result from output layer (reconstructed image) but i cant save or show the compressed copy of image(output of hidden layer)and the final weights of hidden layer please help me?

manel
on 21 Apr 2014

hi ; I have the same problem ; if you have solve it can you pleez say me what you have done. thanks in advance

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